{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O1t22v2ReiTx"
   },
   "source": [
    "# Multi-bootstrap for evaluating pretrained LMs\n",
    "\n",
    "This notebook shows an example of a paired multi-bootstrap analysis. This type of analysis is applicable for any kind of intervention that is applied independently to a particular pretraining (e.g. BERT) checkpoint, including:\n",
    "\n",
    "- Interventions such as intermediate task training or pruning which directly manipulate a pretraining checkpoint.\n",
    "- Changes to any fine-tuning or probing procedure which is applied after pretraining.\n",
    "\n",
    "In the most general case, we'll have a set of $k$ pretraining checkpoints (seeds), to which we'll apply our intervention, perform any additional transformations (like fine-tuning), then evaluate a downstream metric $L$ on a finite evaluation set. The multiple bootstrap procedure allows us to account for three sources of variance:\n",
    "\n",
    "1. Variation between pretraining checkpoints\n",
    "2. Expected variance due to a finite evaluation set\n",
    "3. Variation due to fine-tuning or other procedure\n",
    "\n",
    "## 2M vs. 1M pretraining steps\n",
    "\n",
    "Here, we'll compare the MultiBERTs models run for 2M steps with those run for 1M steps, as described in **Appendix E.1** of [the paper](https://openreview.net/pdf?id=K0E_F0gFDgA). We'll use the five pretraining seeds (0,1,2,3,4) for which we have a dense set of checkpoints throughout training, such that we can treat the 2M runs as an \"intervention\" (training for additional time) over the 1M-step models and perform a paired analysis. From each pretraining checkpoint, we'll run fine-tuning 5 times for each of 4 learning rates, select the best learning rate (treating this as part of the optimization), and then run our multibootstrap procedure.\n",
    "\n",
    "We'll use MultiNLI for this example, but the code below can easily be modified to run on other tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Hz6d1y5qjshN"
   },
   "outputs": [],
   "source": [
    "#@title Import libraries and multibootstrap code\n",
    "import re\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn.metrics\n",
    "import scipy.stats\n",
    "\n",
    "from tqdm.notebook import tqdm  # for progress indicator\n",
    "\n",
    "import multibootstrap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100 19630  100 19630    0     0   175k      0 --:--:-- --:--:-- --:--:--  175k\n",
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100  492k  100  492k    0     0  3020k      0 --:--:-- --:--:-- --:--:-- 3020k\n"
     ]
    }
   ],
   "source": [
    "scratch_dir = \"/tmp/multiberts_mnli\"\n",
    "if not os.path.isdir(scratch_dir): \n",
    "    os.mkdir(scratch_dir)\n",
    "\n",
    "preds_root = \"https://storage.googleapis.com/multiberts/public/example-predictions/GLUE\"\n",
    "# Fetch development set labels\n",
    "!curl -O $preds_root/MNLI_dev_labels --output-dir $scratch_dir\n",
    "# Fetch predictions index file\n",
    "!curl -O $preds_root/index.tsv --output-dir $scratch_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index.tsv  MNLI_dev_labels\r\n"
     ]
    }
   ],
   "source": [
    "!ls $scratch_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the run metadata. You can also just look through the directory, but this index file is convenient if (as we do here) you only want to download some of the files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file</th>\n",
       "      <th>task</th>\n",
       "      <th>pretrain_id</th>\n",
       "      <th>n_steps</th>\n",
       "      <th>lr</th>\n",
       "      <th>ft_seed</th>\n",
       "      <th>release</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1260</th>\n",
       "      <td>MNLI/release=multiberts,pretrain_id=0,n_steps=...</td>\n",
       "      <td>MNLI</td>\n",
       "      <td>0</td>\n",
       "      <td>1M</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>0</td>\n",
       "      <td>multiberts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1261</th>\n",
       "      <td>MNLI/release=multiberts,pretrain_id=0,n_steps=...</td>\n",
       "      <td>MNLI</td>\n",
       "      <td>0</td>\n",
       "      <td>1M</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>1</td>\n",
       "      <td>multiberts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1262</th>\n",
       "      <td>MNLI/release=multiberts,pretrain_id=0,n_steps=...</td>\n",
       "      <td>MNLI</td>\n",
       "      <td>0</td>\n",
       "      <td>1M</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>2</td>\n",
       "      <td>multiberts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1263</th>\n",
       "      <td>MNLI/release=multiberts,pretrain_id=0,n_steps=...</td>\n",
       "      <td>MNLI</td>\n",
       "      <td>0</td>\n",
       "      <td>1M</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>3</td>\n",
       "      <td>multiberts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1264</th>\n",
       "      <td>MNLI/release=multiberts,pretrain_id=0,n_steps=...</td>\n",
       "      <td>MNLI</td>\n",
       "      <td>0</td>\n",
       "      <td>1M</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>4</td>\n",
       "      <td>multiberts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1855</th>\n",
       "      <td>MNLI/release=multiberts,pretrain_id=14,n_steps...</td>\n",
       "      <td>MNLI</td>\n",
       "      <td>14</td>\n",
       "      <td>2M</td>\n",
       "      <td>0.00005</td>\n",
       "      <td>0</td>\n",
       "      <td>multiberts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1856</th>\n",
       "      <td>MNLI/release=multiberts,pretrain_id=14,n_steps...</td>\n",
       "      <td>MNLI</td>\n",
       "      <td>14</td>\n",
       "      <td>2M</td>\n",
       "      <td>0.00005</td>\n",
       "      <td>1</td>\n",
       "      <td>multiberts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1857</th>\n",
       "      <td>MNLI/release=multiberts,pretrain_id=14,n_steps...</td>\n",
       "      <td>MNLI</td>\n",
       "      <td>14</td>\n",
       "      <td>2M</td>\n",
       "      <td>0.00005</td>\n",
       "      <td>2</td>\n",
       "      <td>multiberts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1858</th>\n",
       "      <td>MNLI/release=multiberts,pretrain_id=14,n_steps...</td>\n",
       "      <td>MNLI</td>\n",
       "      <td>14</td>\n",
       "      <td>2M</td>\n",
       "      <td>0.00005</td>\n",
       "      <td>3</td>\n",
       "      <td>multiberts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1859</th>\n",
       "      <td>MNLI/release=multiberts,pretrain_id=14,n_steps...</td>\n",
       "      <td>MNLI</td>\n",
       "      <td>14</td>\n",
       "      <td>2M</td>\n",
       "      <td>0.00005</td>\n",
       "      <td>4</td>\n",
       "      <td>multiberts</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   file  task  pretrain_id  \\\n",
       "1260  MNLI/release=multiberts,pretrain_id=0,n_steps=...  MNLI            0   \n",
       "1261  MNLI/release=multiberts,pretrain_id=0,n_steps=...  MNLI            0   \n",
       "1262  MNLI/release=multiberts,pretrain_id=0,n_steps=...  MNLI            0   \n",
       "1263  MNLI/release=multiberts,pretrain_id=0,n_steps=...  MNLI            0   \n",
       "1264  MNLI/release=multiberts,pretrain_id=0,n_steps=...  MNLI            0   \n",
       "...                                                 ...   ...          ...   \n",
       "1855  MNLI/release=multiberts,pretrain_id=14,n_steps...  MNLI           14   \n",
       "1856  MNLI/release=multiberts,pretrain_id=14,n_steps...  MNLI           14   \n",
       "1857  MNLI/release=multiberts,pretrain_id=14,n_steps...  MNLI           14   \n",
       "1858  MNLI/release=multiberts,pretrain_id=14,n_steps...  MNLI           14   \n",
       "1859  MNLI/release=multiberts,pretrain_id=14,n_steps...  MNLI           14   \n",
       "\n",
       "     n_steps       lr  ft_seed     release  \n",
       "1260      1M  0.00002        0  multiberts  \n",
       "1261      1M  0.00002        1  multiberts  \n",
       "1262      1M  0.00002        2  multiberts  \n",
       "1263      1M  0.00002        3  multiberts  \n",
       "1264      1M  0.00002        4  multiberts  \n",
       "...      ...      ...      ...         ...  \n",
       "1855      2M  0.00005        0  multiberts  \n",
       "1856      2M  0.00005        1  multiberts  \n",
       "1857      2M  0.00005        2  multiberts  \n",
       "1858      2M  0.00005        3  multiberts  \n",
       "1859      2M  0.00005        4  multiberts  \n",
       "\n",
       "[600 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task_name = \"MNLI\"\n",
    "\n",
    "run_info = pd.read_csv(os.path.join(scratch_dir, 'index.tsv'), sep='\\t')\n",
    "\n",
    "# Filter to the runs we're interested in\n",
    "mask = run_info.task == task_name\n",
    "mask &= run_info.release == 'multiberts'\n",
    "run_info = run_info[mask].copy()\n",
    "run_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the dev set labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "UryctX2xf5xe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tasks: ['MNLI']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'MNLI': 9815}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ALL_TASKS = list(run_info.task.unique())\n",
    "print(\"Tasks:\", ALL_TASKS)\n",
    "\n",
    "task_labels = {}\n",
    "for task_name in ALL_TASKS:\n",
    "    labels_file = os.path.join(scratch_dir, task_name + \"_dev_labels\")\n",
    "    labels = np.loadtxt(labels_file).astype(float if task_name == 'STS-B' else int)\n",
    "    task_labels[task_name] = labels\n",
    "\n",
    "{k:len(v) for k, v in task_labels.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0bc42328309a4e74b6c19f558463f4fe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/600 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Download all prediction files\n",
    "for fname in tqdm(run_info.file):\n",
    "    !curl $preds_root/$fname -o $scratch_dir/$fname --create-dirs --silent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'release=multiberts,pretrain_id=0,n_steps=1M,lr=2e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=1M,lr=2e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=1M,lr=2e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=1M,lr=2e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=1M,lr=2e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=1M,lr=3e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=1M,lr=3e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=1M,lr=3e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=1M,lr=3e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=1M,lr=3e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=1M,lr=4e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=1M,lr=4e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=1M,lr=4e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=1M,lr=4e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=1M,lr=4e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=1M,lr=5e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=1M,lr=5e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=1M,lr=5e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=1M,lr=5e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=1M,lr=5e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=2M,lr=2e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=2M,lr=2e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=2M,lr=2e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=2M,lr=2e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=2M,lr=2e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=2M,lr=3e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=2M,lr=3e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=2M,lr=3e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=2M,lr=3e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=2M,lr=3e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=2M,lr=4e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=2M,lr=4e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=2M,lr=4e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=2M,lr=4e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=2M,lr=4e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=2M,lr=5e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=2M,lr=5e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=2M,lr=5e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=2M,lr=5e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=0,n_steps=2M,lr=5e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=10,n_steps=2M,lr=2e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=10,n_steps=2M,lr=2e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=10,n_steps=2M,lr=2e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=10,n_steps=2M,lr=2e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=10,n_steps=2M,lr=2e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=10,n_steps=2M,lr=3e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=10,n_steps=2M,lr=3e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=10,n_steps=2M,lr=3e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=10,n_steps=2M,lr=3e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=10,n_steps=2M,lr=3e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=10,n_steps=2M,lr=4e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=10,n_steps=2M,lr=4e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=10,n_steps=2M,lr=4e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=10,n_steps=2M,lr=4e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=10,n_steps=2M,lr=4e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=10,n_steps=2M,lr=5e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=10,n_steps=2M,lr=5e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=10,n_steps=2M,lr=5e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=10,n_steps=2M,lr=5e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=10,n_steps=2M,lr=5e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=11,n_steps=2M,lr=2e-05,ft_seed=0.tsv'\r\n",
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      "'release=multiberts,pretrain_id=2,n_steps=2M,lr=5e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=1M,lr=2e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=1M,lr=2e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=1M,lr=2e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=1M,lr=2e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=1M,lr=2e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=1M,lr=3e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=1M,lr=3e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=1M,lr=3e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=1M,lr=3e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=1M,lr=3e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=1M,lr=4e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=1M,lr=4e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=1M,lr=4e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=1M,lr=4e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=1M,lr=4e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=1M,lr=5e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=1M,lr=5e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=1M,lr=5e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=1M,lr=5e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=1M,lr=5e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=2M,lr=2e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=2M,lr=2e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=2M,lr=2e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=2M,lr=2e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=2M,lr=2e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=2M,lr=3e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=2M,lr=3e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=2M,lr=3e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=2M,lr=3e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=2M,lr=3e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=2M,lr=4e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=2M,lr=4e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=2M,lr=4e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=2M,lr=4e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=2M,lr=4e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=2M,lr=5e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=2M,lr=5e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=2M,lr=5e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=2M,lr=5e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=3,n_steps=2M,lr=5e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=1M,lr=2e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=1M,lr=2e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=1M,lr=2e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=1M,lr=2e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=1M,lr=2e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=1M,lr=3e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=1M,lr=3e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=1M,lr=3e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=1M,lr=3e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=1M,lr=3e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=1M,lr=4e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=1M,lr=4e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=1M,lr=4e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=1M,lr=4e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=1M,lr=4e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=1M,lr=5e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=1M,lr=5e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=1M,lr=5e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=1M,lr=5e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=1M,lr=5e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=2M,lr=2e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=2M,lr=2e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=2M,lr=2e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=2M,lr=2e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=2M,lr=2e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=2M,lr=3e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=2M,lr=3e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=2M,lr=3e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=2M,lr=3e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=2M,lr=3e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=2M,lr=4e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=2M,lr=4e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=2M,lr=4e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=2M,lr=4e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=2M,lr=4e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=2M,lr=5e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=2M,lr=5e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=2M,lr=5e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=2M,lr=5e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=4,n_steps=2M,lr=5e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=5,n_steps=2M,lr=2e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=5,n_steps=2M,lr=2e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=5,n_steps=2M,lr=2e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=5,n_steps=2M,lr=2e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=5,n_steps=2M,lr=2e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=5,n_steps=2M,lr=3e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=5,n_steps=2M,lr=3e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=5,n_steps=2M,lr=3e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=5,n_steps=2M,lr=3e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=5,n_steps=2M,lr=3e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=5,n_steps=2M,lr=4e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=5,n_steps=2M,lr=4e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=5,n_steps=2M,lr=4e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=5,n_steps=2M,lr=4e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=5,n_steps=2M,lr=4e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=5,n_steps=2M,lr=5e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=5,n_steps=2M,lr=5e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=5,n_steps=2M,lr=5e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=5,n_steps=2M,lr=5e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=5,n_steps=2M,lr=5e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=6,n_steps=2M,lr=2e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=6,n_steps=2M,lr=2e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=6,n_steps=2M,lr=2e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=6,n_steps=2M,lr=2e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=6,n_steps=2M,lr=2e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=6,n_steps=2M,lr=3e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=6,n_steps=2M,lr=3e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=6,n_steps=2M,lr=3e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=6,n_steps=2M,lr=3e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=6,n_steps=2M,lr=3e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=6,n_steps=2M,lr=4e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=6,n_steps=2M,lr=4e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=6,n_steps=2M,lr=4e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=6,n_steps=2M,lr=4e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=6,n_steps=2M,lr=4e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=6,n_steps=2M,lr=5e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=6,n_steps=2M,lr=5e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=6,n_steps=2M,lr=5e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=6,n_steps=2M,lr=5e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=6,n_steps=2M,lr=5e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=7,n_steps=2M,lr=2e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=7,n_steps=2M,lr=2e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=7,n_steps=2M,lr=2e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=7,n_steps=2M,lr=2e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=7,n_steps=2M,lr=2e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=7,n_steps=2M,lr=3e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=7,n_steps=2M,lr=3e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=7,n_steps=2M,lr=3e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=7,n_steps=2M,lr=3e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=7,n_steps=2M,lr=3e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=7,n_steps=2M,lr=4e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=7,n_steps=2M,lr=4e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=7,n_steps=2M,lr=4e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=7,n_steps=2M,lr=4e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=7,n_steps=2M,lr=4e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=7,n_steps=2M,lr=5e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=7,n_steps=2M,lr=5e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=7,n_steps=2M,lr=5e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=7,n_steps=2M,lr=5e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=7,n_steps=2M,lr=5e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=8,n_steps=2M,lr=2e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=8,n_steps=2M,lr=2e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=8,n_steps=2M,lr=2e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=8,n_steps=2M,lr=2e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=8,n_steps=2M,lr=2e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=8,n_steps=2M,lr=3e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=8,n_steps=2M,lr=3e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=8,n_steps=2M,lr=3e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=8,n_steps=2M,lr=3e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=8,n_steps=2M,lr=3e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=8,n_steps=2M,lr=4e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=8,n_steps=2M,lr=4e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=8,n_steps=2M,lr=4e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=8,n_steps=2M,lr=4e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=8,n_steps=2M,lr=4e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=8,n_steps=2M,lr=5e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=8,n_steps=2M,lr=5e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=8,n_steps=2M,lr=5e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=8,n_steps=2M,lr=5e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=8,n_steps=2M,lr=5e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=9,n_steps=2M,lr=2e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=9,n_steps=2M,lr=2e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=9,n_steps=2M,lr=2e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=9,n_steps=2M,lr=2e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=9,n_steps=2M,lr=2e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=9,n_steps=2M,lr=3e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=9,n_steps=2M,lr=3e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=9,n_steps=2M,lr=3e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=9,n_steps=2M,lr=3e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=9,n_steps=2M,lr=3e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=9,n_steps=2M,lr=4e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=9,n_steps=2M,lr=4e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=9,n_steps=2M,lr=4e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=9,n_steps=2M,lr=4e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=9,n_steps=2M,lr=4e-05,ft_seed=4.tsv'\r\n",
      "'release=multiberts,pretrain_id=9,n_steps=2M,lr=5e-05,ft_seed=0.tsv'\r\n",
      "'release=multiberts,pretrain_id=9,n_steps=2M,lr=5e-05,ft_seed=1.tsv'\r\n",
      "'release=multiberts,pretrain_id=9,n_steps=2M,lr=5e-05,ft_seed=2.tsv'\r\n",
      "'release=multiberts,pretrain_id=9,n_steps=2M,lr=5e-05,ft_seed=3.tsv'\r\n",
      "'release=multiberts,pretrain_id=9,n_steps=2M,lr=5e-05,ft_seed=4.tsv'\r\n"
     ]
    }
   ],
   "source": [
    "!ls $scratch_dir/MNLI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "yuw4KYCugOtA"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c2e5777c3801422c80f03346cc625edf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/600 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load all predictions (slow)\n",
    "def get_preds(preds_tsv_path, task_name):\n",
    "    \"\"\"Load predictions, as array of integers.\"\"\"\n",
    "    # [num_examples, num_classes]\n",
    "    preds = np.loadtxt(preds_tsv_path, delimiter=\"\\t\")\n",
    "    # [num_examples]\n",
    "    if task_name == 'STS-B':\n",
    "        return preds.astype(float)\n",
    "    else:\n",
    "        return np.argmax(preds, axis=1).astype(int)\n",
    "\n",
    "all_preds = [get_preds(os.path.join(scratch_dir, row['file']), row['task']) \n",
    "             for _, row in tqdm(run_info.iterrows(), total=len(run_info))]\n",
    "run_info['preds'] = all_preds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the overall score for each run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "UY_XRY5ggVu_"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "05fc5d720e114773991560389b5b0a47",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/600 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def score_row(task, preds, **kw):\n",
    "    labels = task_labels[task]\n",
    "    if task == \"STS-B\":\n",
    "        metric = lambda x, y: scipy.stats.pearsonr(x, y)[0]\n",
    "    else:\n",
    "        metric = sklearn.metrics.accuracy_score\n",
    "    return metric(preds, labels)\n",
    "\n",
    "all_scores = [score_row(**row) for _, row in tqdm(run_info.iterrows(), total=len(run_info))]\n",
    "run_info['score'] = all_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GV0HbhOthYXa"
   },
   "source": [
    "We treat the selection of fine-tuning learning rate as part of the optimiztion process, and select the best learning rate for each task. Do this independently for each pretraining configuration: original BERT, MultiBERTs 1M, and MultiBERTs 2M."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "Qw8_mZCXg0UQ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "release     n_steps  task\n",
       "multiberts  1M       MNLI    0.00003\n",
       "            2M       MNLI    0.00002\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the best finetuning LR for each task\n",
    "def find_best_lr(sub_df):\n",
    "    return sub_df.groupby('lr').agg({'score': np.mean})['score'].idxmax()\n",
    "\n",
    "gb = run_info.groupby(['release', 'n_steps', 'task'])\n",
    "best_lr = gb.apply(find_best_lr)\n",
    "best_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "tM2I10i_hNFC"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file</th>\n",
       "      <th>task</th>\n",
       "      <th>pretrain_id</th>\n",
       "      <th>n_steps</th>\n",
       "      <th>lr</th>\n",
       "      <th>ft_seed</th>\n",
       "      <th>release</th>\n",
       "      <th>preds</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MNLI/release=multiberts,pretrain_id=0,n_steps=...</td>\n",
       "      <td>MNLI</td>\n",
       "      <td>0</td>\n",
       "      <td>1M</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>0</td>\n",
       "      <td>multiberts</td>\n",
       "      <td>[2, 0, 1, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 1, 2, ...</td>\n",
       "      <td>0.840448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MNLI/release=multiberts,pretrain_id=0,n_steps=...</td>\n",
       "      <td>MNLI</td>\n",
       "      <td>0</td>\n",
       "      <td>1M</td>\n",
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       "      <td>1</td>\n",
       "      <td>multiberts</td>\n",
       "      <td>[2, 0, 1, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 1, 0, ...</td>\n",
       "      <td>0.837086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MNLI/release=multiberts,pretrain_id=0,n_steps=...</td>\n",
       "      <td>MNLI</td>\n",
       "      <td>0</td>\n",
       "      <td>1M</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>2</td>\n",
       "      <td>multiberts</td>\n",
       "      <td>[2, 0, 1, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 1, 0, ...</td>\n",
       "      <td>0.834845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MNLI/release=multiberts,pretrain_id=0,n_steps=...</td>\n",
       "      <td>MNLI</td>\n",
       "      <td>0</td>\n",
       "      <td>1M</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>3</td>\n",
       "      <td>multiberts</td>\n",
       "      <td>[2, 0, 1, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 1, 0, ...</td>\n",
       "      <td>0.839124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MNLI/release=multiberts,pretrain_id=0,n_steps=...</td>\n",
       "      <td>MNLI</td>\n",
       "      <td>0</td>\n",
       "      <td>1M</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>4</td>\n",
       "      <td>multiberts</td>\n",
       "      <td>[2, 0, 1, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 1, 2, ...</td>\n",
       "      <td>0.839633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>MNLI/release=multiberts,pretrain_id=14,n_steps...</td>\n",
       "      <td>MNLI</td>\n",
       "      <td>14</td>\n",
       "      <td>2M</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>0</td>\n",
       "      <td>multiberts</td>\n",
       "      <td>[2, 0, 1, 0, 0, 0, 0, 2, 0, 2, 2, 0, 0, 1, 2, ...</td>\n",
       "      <td>0.842384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>MNLI/release=multiberts,pretrain_id=14,n_steps...</td>\n",
       "      <td>MNLI</td>\n",
       "      <td>14</td>\n",
       "      <td>2M</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>1</td>\n",
       "      <td>multiberts</td>\n",
       "      <td>[2, 0, 1, 0, 0, 0, 0, 2, 0, 2, 2, 0, 0, 1, 2, ...</td>\n",
       "      <td>0.844320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>MNLI/release=multiberts,pretrain_id=14,n_steps...</td>\n",
       "      <td>MNLI</td>\n",
       "      <td>14</td>\n",
       "      <td>2M</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>2</td>\n",
       "      <td>multiberts</td>\n",
       "      <td>[2, 0, 1, 0, 0, 0, 0, 2, 0, 2, 1, 0, 0, 1, 2, ...</td>\n",
       "      <td>0.845746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>MNLI/release=multiberts,pretrain_id=14,n_steps...</td>\n",
       "      <td>MNLI</td>\n",
       "      <td>14</td>\n",
       "      <td>2M</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>3</td>\n",
       "      <td>multiberts</td>\n",
       "      <td>[2, 0, 1, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 1, 2, ...</td>\n",
       "      <td>0.841263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>MNLI/release=multiberts,pretrain_id=14,n_steps...</td>\n",
       "      <td>MNLI</td>\n",
       "      <td>14</td>\n",
       "      <td>2M</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>4</td>\n",
       "      <td>multiberts</td>\n",
       "      <td>[2, 0, 1, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 1, 2, ...</td>\n",
       "      <td>0.848497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  file  task  pretrain_id  \\\n",
       "0    MNLI/release=multiberts,pretrain_id=0,n_steps=...  MNLI            0   \n",
       "1    MNLI/release=multiberts,pretrain_id=0,n_steps=...  MNLI            0   \n",
       "2    MNLI/release=multiberts,pretrain_id=0,n_steps=...  MNLI            0   \n",
       "3    MNLI/release=multiberts,pretrain_id=0,n_steps=...  MNLI            0   \n",
       "4    MNLI/release=multiberts,pretrain_id=0,n_steps=...  MNLI            0   \n",
       "..                                                 ...   ...          ...   \n",
       "145  MNLI/release=multiberts,pretrain_id=14,n_steps...  MNLI           14   \n",
       "146  MNLI/release=multiberts,pretrain_id=14,n_steps...  MNLI           14   \n",
       "147  MNLI/release=multiberts,pretrain_id=14,n_steps...  MNLI           14   \n",
       "148  MNLI/release=multiberts,pretrain_id=14,n_steps...  MNLI           14   \n",
       "149  MNLI/release=multiberts,pretrain_id=14,n_steps...  MNLI           14   \n",
       "\n",
       "    n_steps       lr  ft_seed     release  \\\n",
       "0        1M  0.00003        0  multiberts   \n",
       "1        1M  0.00003        1  multiberts   \n",
       "2        1M  0.00003        2  multiberts   \n",
       "3        1M  0.00003        3  multiberts   \n",
       "4        1M  0.00003        4  multiberts   \n",
       "..      ...      ...      ...         ...   \n",
       "145      2M  0.00002        0  multiberts   \n",
       "146      2M  0.00002        1  multiberts   \n",
       "147      2M  0.00002        2  multiberts   \n",
       "148      2M  0.00002        3  multiberts   \n",
       "149      2M  0.00002        4  multiberts   \n",
       "\n",
       "                                                 preds     score  \n",
       "0    [2, 0, 1, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 1, 2, ...  0.840448  \n",
       "1    [2, 0, 1, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 1, 0, ...  0.837086  \n",
       "2    [2, 0, 1, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 1, 0, ...  0.834845  \n",
       "3    [2, 0, 1, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 1, 0, ...  0.839124  \n",
       "4    [2, 0, 1, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 1, 2, ...  0.839633  \n",
       "..                                                 ...       ...  \n",
       "145  [2, 0, 1, 0, 0, 0, 0, 2, 0, 2, 2, 0, 0, 1, 2, ...  0.842384  \n",
       "146  [2, 0, 1, 0, 0, 0, 0, 2, 0, 2, 2, 0, 0, 1, 2, ...  0.844320  \n",
       "147  [2, 0, 1, 0, 0, 0, 0, 2, 0, 2, 1, 0, 0, 1, 2, ...  0.845746  \n",
       "148  [2, 0, 1, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 1, 2, ...  0.841263  \n",
       "149  [2, 0, 1, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 1, 2, ...  0.848497  \n",
       "\n",
       "[150 rows x 9 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select only runs with the best LR (should be 1/4 of total)\n",
    "gb = run_info.groupby(['release', 'n_steps', 'task'])\n",
    "\n",
    "def filter_to_best_lr(sub_df):\n",
    "    lr = find_best_lr(sub_df)\n",
    "    return sub_df[sub_df.lr == lr]\n",
    "\n",
    "best_lr_runs = gb.apply(filter_to_best_lr).reset_index(drop=True)\n",
    "best_lr_runs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5sPsiEiShk3E"
   },
   "source": [
    "## Run multibootstrap (paired)\n",
    "\n",
    "base (`L`) is MultiBERTs with 1M steps, expt (`L'`) is MultiBERTs with 2M steps. We have five seeds on base, and 25 seeds on expt but only five of these correspond to base seeds, which the `multibootstrap()` code will select automatically. Each seed will have 5 finetuning runs, which will be averaged over inside each sample.\n",
    "\n",
    "Note that while in the paper we focus on metrics like accuracy that can be expressed as an average point loss, in general the multibootstrap procedure is valid for most common metrics like F1 or BLEU that behave asymptotically like one. As such, our API takes an arbitrary metric function `f(y_pred, y_true)` which will be called on each sample. See the docstring in `multibootstrap.py` for more detail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "cklZ--NShOOd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Task:  MNLI\n",
      "Available runs: 150\n",
      "Labels: int64 (9815,)\n",
      "Preds: int64 (150, 9815)\n",
      "Multibootstrap (paired) on 9815 examples\n",
      "  Common seeds (5): [0, 1, 2, 3, 4]\n",
      "  Warning: 20 seeds found on expt side only. These will be omitted from the analysis: {5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1f86ef459d4c45a0827c5d75e5ba66a7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bootstrap statistics from 1000 samples:\n",
      "  E[L]  = 0.837 with 95% CI of (0.831 to 0.844)\n",
      "  E[L'] = 0.844 with 95% CI of (0.838 to 0.85)\n",
      "  E[L'-L] = 0.007 with 95% CI of (0.0039 to 0.00991); p-value = 0\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">MNLI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>base</th>\n",
       "      <td>0.837332</td>\n",
       "      <td>0.831248</td>\n",
       "      <td>0.843770</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>expt</th>\n",
       "      <td>0.844328</td>\n",
       "      <td>0.838156</td>\n",
       "      <td>0.850376</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta</th>\n",
       "      <td>0.006996</td>\n",
       "      <td>0.003896</td>\n",
       "      <td>0.009907</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           MNLI                         \n",
       "           mean       low      high    p\n",
       "base   0.837332  0.831248  0.843770  NaN\n",
       "expt   0.844328  0.838156  0.850376  NaN\n",
       "delta  0.006996  0.003896  0.009907  0.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_bootstrap_samples = 1000 #@param {type: \"integer\"}\n",
    "\n",
    "mask = (best_lr_runs.release == 'multiberts')\n",
    "run_df = best_lr_runs[mask]\n",
    "\n",
    "stats = {}\n",
    "for task_name in ALL_TASKS:\n",
    "    print(\"Task: \", task_name)\n",
    "    selected_runs = run_df[run_df.task == task_name].copy()\n",
    "    # Set intervention and seed columns\n",
    "    selected_runs['intervention'] = selected_runs.n_steps == '2M'\n",
    "    selected_runs['seed'] = selected_runs.pretrain_id\n",
    "    print(\"Available runs:\", len(selected_runs))\n",
    "\n",
    "    labels = task_labels[task_name]\n",
    "    print(\"Labels:\", labels.dtype, labels.shape)\n",
    "    preds = np.stack(selected_runs.preds)\n",
    "    print(\"Preds:\", preds.dtype, preds.shape)\n",
    "\n",
    "    if task_name == \"STS-B\":\n",
    "        metric = lambda x, y: scipy.stats.pearsonr(x, y)[0]\n",
    "    else:\n",
    "        metric = sklearn.metrics.accuracy_score\n",
    "\n",
    "    samples = multibootstrap.multibootstrap(selected_runs, preds, labels,\n",
    "                                            metric, nboot=num_bootstrap_samples,\n",
    "                                            paired_seeds=True,\n",
    "                                            progress_indicator=tqdm)\n",
    "    stats[task_name] = multibootstrap.report_ci(samples, c=0.95)\n",
    "    print(\"\")  # newline\n",
    "\n",
    "pd.concat({k: pd.DataFrame(v) for k,v in stats.items()}).transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot result distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "OCD8x6GMh9IQ"
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "sns.set_style('white')\n",
    "%config InlineBackend.figure_format = 'retina' # make matplotlib plots look better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "VStbUkDUjoNp"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'MultiBERTs 1M vs 2M'}, xlabel='Pretraining Steps', ylabel='MNLI Accuracy'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 439,
       "width": 617
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot distribution of scores\n",
    "var_name = 'Pretraining Steps'\n",
    "val_name = \"MNLI Accuracy\"\n",
    "bdf = pd.DataFrame(samples, columns=['1M', '2M']).melt(var_name=var_name, value_name=val_name)\n",
    "bdf['x'] = 0\n",
    "fig = pyplot.figure(figsize=(10, 7))\n",
    "ax = fig.gca()\n",
    "sns.violinplot(ax=ax, x=var_name, y=val_name, data=bdf, inner='quartile')\n",
    "ax.set_title(\"MultiBERTs 1M vs 2M\")\n",
    "ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hyDqHkLHsPkO"
   },
   "source": [
    "The distributions of scores from 1M and 2M checkpoints seem to overlap significantly in the above plot, but because these are derived from the same samples of (seeds, examples), they are highly correlated. If we look at deltas, we see that in nearly all cases, the intervention (pretraining to 2M steps) will outperform the base model (1M steps), confirming the p-value of close to zero we computed above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "wZ7WImtmrn71"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'MultiBERTs 1M vs 2M'}, xlabel='Pretraining Steps', ylabel='MNLI Accuracy delta'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 439,
       "width": 338
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot distribution of deltas L' - L\n",
    "var_name = 'Pretraining Steps'\n",
    "val_name = \"MNLI Accuracy delta\"\n",
    "bdf = pd.DataFrame(samples, columns=['1M', '2M'])\n",
    "bdf['deltas'] = bdf['2M'] - bdf['1M']\n",
    "bdf = bdf.drop(axis=1, labels=['1M', '2M']).melt(var_name=var_name, value_name=val_name)\n",
    "bdf['x'] = 0\n",
    "fig = pyplot.figure(figsize=(5, 7))\n",
    "ax = fig.gca()\n",
    "sns.violinplot(ax=ax, x=var_name, y=val_name, data=bdf, inner='quartile',\n",
    "               palette='gray')\n",
    "ax.set_title(\"MultiBERTs 1M vs 2M\")\n",
    "# ax.set_ylim(bottom=0)\n",
    "ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plots the above side-by-side, to create Figure 4 from the paper. Note that the shapes won't match exactly due to randomness, but should be qualitatively similar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "LrzL_425l2qV"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pretraining Steps', ylabel='MNLI Accuracy delta'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 450
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot distribution of results and deltas\n",
    "fig, (a1, a2) = pyplot.subplots(nrows=1, ncols=2, figsize=(7,4), \n",
    "                                gridspec_kw=dict(width_ratios=[2,1], wspace=0.33))\n",
    "\n",
    "\n",
    "var_name = 'Pretraining Steps'\n",
    "val_name = \"MNLI Accuracy\"\n",
    "bdf = pd.DataFrame(samples, columns=['1M', '2M']).melt(var_name=var_name, value_name=val_name)\n",
    "bdf['x'] = 0\n",
    "sns.violinplot(ax=a1, x=var_name, y=val_name, data=bdf, inner='quartile')\n",
    "\n",
    "var_name = 'Pretraining Steps'\n",
    "val_name = \"MNLI Accuracy delta\"\n",
    "bdf = pd.DataFrame(samples, columns=['1M', '2M'])\n",
    "bdf['deltas'] = bdf['2M'] - bdf['1M']\n",
    "bdf = bdf.drop(axis=1, labels=['1M', '2M']).melt(var_name=var_name, value_name=val_name)\n",
    "bdf['x'] = 0\n",
    "sns.violinplot(ax=a2, x=var_name, y=val_name, data=bdf, inner='quartile',\n",
    "               palette='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sY5bQnphkqrI"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "last_runtime": {},
   "name": "Example: 2M vs 1M steps",
   "private_outputs": true,
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
